Traditional media measurement systems have focused on directly monitoring channels being utilized by audience members. However, as media consumption patterns have become more complex, channel-centric media measurement is inadequate for many purposes. It may be desirable to track usage of particular media content independent of channel. Furthermore, although “channel” identification in a traditional media measurement system may sometimes be limited to radio or television broadcast station, it is increasingly desirable to track usage of media across several types of media delivery vehicles including radio, television, CD, DVD, computer download, portable media players (e.g. MP3 players, iPod), and other vehicles. Furthermore, with respect to tracking consumption of advertisements, it may be inadequate to simply track channel tuning, because, for example, an audience member may mute a broadcast during commercial periods. Thus simply identifying a broadcast channel does not adequately track whether the audience member listened to a particular advertisement.
Some media measurement systems have used codes to “tag” and track particular content. However, such systems are limited in that they can only track content that has been properly encoded.
With the development of more robust content recognition technologies, some content recognition systems have recently been deployed which do not rely on codes. For example, Philips, Shazam Entertainment, and others have marketed systems for identifying songs played into a mobile phone. Although such systems can be efficiently deployed in the context of song recognition, deploying such systems in the context of media measurement systems poses particular challenges. Continuous searching against a large database of media content can be computationally intensive. Furthermore, such systems, while increasingly robust, still return some erroneous results, particularly in high-noise environments.
At the same time, the media measurement context provides opportunities to utilize data exogenous to a particular audio or video data sample. Such opportunities have thus far been insufficiently exploited for the purpose of efficiently applying existing content recognition technologies in the media measurement context. Thus, an improved media measurement system and method is needed.